Search result: Catalogue data in Autumn Semester 2016
|Computational Science and Engineering Master|
|Fields of Specialization|
|Systems and Control|
|227-0103-00L||Control Systems||W||6 credits||2V + 2U||F. Dörfler|
|Abstract||Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.|
|Objective||Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.|
|Content||Process automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.|
|Literature||K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.|
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
|Prerequisites / Notice||Prerequisites: Signal and Systems Theory II. |
MATLAB is used for system analysis and simulation.
|227-0045-00L||Signals and Systems I||W||4 credits||2V + 2U||H. Bölcskei|
|Abstract||Signal theory and systems theory (continuous-time and discrete-time): Signal analysis in the time and frequency domains, signal spaces, Hilbert spaces, generalized functions, linear time-invariant systems, sampling theorems, discrete-time signals and systems, digital filter structures, Discrete Fourier Transform (DFT), finite-dimensional signals and systems, Fast Fourier Transform (FFT).|
|Objective||Introduction to mathematical signal processing and system theory.|
|Content||Signal theory and systems theory (continuous-time and discrete-time): Signal analysis in the time and frequency domains, signal spaces, Hilbert spaces, generalized functions, linear time-invariant systems, sampling theorems, discrete-time signals and systems, digital filter structures, Discrete Fourier Transform (DFT), finite-dimensional signals and systems, Fast Fourier Transform (FFT).|
|Lecture notes||Lecture notes, problem set with solutions.|
|227-0225-00L||Linear System Theory||W||6 credits||5G||M. Kamgarpour|
|Abstract||The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, their use in control, filtering, and estimation and their applications to areas ranging from avionics to systems biology.|
|Objective||By the end of the class students should be comfortable with the fundamental results in linear system theory and the mathematical tools used to derive them.|
|Content||- Rings, fields and linear spaces, normed linear spaces and inner product spaces.|
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete time, time varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, canonical forms, Kalman decomposition. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
- Realization theory.
|Lecture notes||F.M. Callier and C.A. Desoer, "Linear System Theory", Springer-Verlag, 1991.|
|Prerequisites / Notice||Prerequisites: Control Systems I (227-0103-00) or equivalent and sufficient mathematical maturity.|
|252-0535-00L||Machine Learning||W||8 credits||3V + 2U + 2A||J. M. Buhmann|
|Abstract||Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.|
|Objective||Students will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data.|
|Content||The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.|
Topics covered in the lecture include:
- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
|Lecture notes||No lecture notes, but slides will be made available on the course webpage.|
|Literature||C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.|
R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.
L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
|Prerequisites / Notice||The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.|
Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
|151-0575-01L||Signals and Systems||W||4 credits||2V + 2U||R. D'Andrea|
|Abstract||Signals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.|
|Objective||Master the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.|
|Content||Discrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.|
|Lecture notes||Lecture notes available on course website.|
|151-0563-01L||Dynamic Programming and Optimal Control||W||4 credits||2V + 1U||R. D'Andrea|
|Abstract||Introduction to Dynamic Programming and Optimal Control.|
|Objective||Covers the fundamental concepts of Dynamic Programming & Optimal Control.|
|Content||Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.|
|Literature||Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.|
|Prerequisites / Notice||Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.|
|401-5850-00L||Seminar in Systems and Control for CSE||W||4 credits||2S||J. Lygeros|
- Page 1 of 1